Two new papers!

Warning: if you’re on the ICFP program committee and want to preserve double-blind reviewing, please don’t read this post.

Phew! The ICFP deadline is past, and two papers have been submitted.

The first paper covers the full story of stream fusion, greatly extending work initially done for the ByteString library, in particular, how to fuse zips, concatMaps and list comprehensions, and we’ve implemented the entire list library to be stream fusible. The point: faster Haskell code.

The second paper describes how, using Haskell, we can write monte-carlo-based simulators that out perform optimised C implementations, using a generative approach to simulator specialisation. In particular, we generate multicore and cluster-based simulators for polymer chemistry, outperforming the existing (commercial) software in this area.

This paper presents an automatic fusion system, stream fusion, based on equational transformations, that fuses a wider range of functions than existing short-cut fusion systems. In particular, stream fusion is able to fuse zips, left folds and functions over nested lists, including list comprehensions. A distinguishing feature of the stream fusion framework is its simplicity: by transforming list functions to expose their structure, intermediate values are eliminated by general purpose compiler optimisations.

We have reimplemented the entire Haskell standard list library on top of our framework, providing stream fusion for Haskell lists. By allowing a wider range of functions to fuse, we see an increase in the number of occurrences of fusion in typical Haskell programs. We present benchmarks documenting time and space improvements.

We address the tension between software generality and performance in the domain of scientific and financial simulations based on Monte-Carlo methods. To this end, we present a novel software architecture, centred around the concept of a specialising simulator generator, that combines and extends methods from generative programming, partial evaluation, runtime code generation, and dynamic code loading. The core tenet is that, given a fixed simulator configuration, a generator in a functional language can produce low-level code that is more highly optimised than a manually implemented generic simulator. We also introduce a skeleton, or template, capturing a wide range of Monte-Carlo methods and use it to explain how to design specialising simulator generators and how to generate parallelised simulators for multi-core and distributed-memory multiprocessors.

We evaluated the practical benefits and limitations of our approach by applying it to a highly relevant problem in computational chemistry. More precisely, we used a Markov-chain Monte-Carlo method for the study of advanced forms of polymerisation kinetics. The resulting implementation executes faster than all competing software products, while at the same time also being more general. The generative architecture allows us to cover a wider range of chemical reactions and to target a wider range of high-performance architectures (such as PC clusters and SMP multiprocessors).

We show that it is possible to outperform low-level languages with functional programming in domains with very stringent performance requirements if the domain also demands generality.